a bayesian partition method for detecting pleiotropic and epistatic eqtl modules一个贝叶斯分区方法检测多效性的和上位eqtl模块.pdf

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules 1 2,3 3,4 5 Wei Zhang , Jun Zhu , Eric E. Schadt , Jun S. Liu * 1 UBS Equities, Stamford, Connecticut, United States of America, 2 Rosetta Inpharmatics, LLC, Merck Co., Inc., Seattle, Washington, United States of America, 3 Sage Bionetworks, Seattle, Washington, United States of America, 4 Pacific Biosciences, Menlo Park, California, United States of America, 5 Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America Abstract Studies of the relationship between DNA variation and gene expression variation, often referred to as ‘‘expression quantitative trait loci (eQTL) mapping’’, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules